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This paper investigates online evaluation processes on peer-to-peer platforms to highlight how online peer evaluation enacts neoliberal subjects and collectives.

The paper uses netnography (Kozinets, 2002) to study the online community of Airbnb. It is also based on 18 interviews, mostly with Airbnb users, and quantitative data about reviews.

Results indicate that peer-to-peer platforms constitute biopolitical infrastructures. They enact and consolidate narcissistic entrepreneurs of the self through evaluation processes and consolidating a for-show community. Specifically, three features make evaluation a powerful neoliberal agent. The object of evaluation shifts from the service to the user's own worth (1). The public nature of the evaluation (2) and symetrical accountability between the evaluator and the evaluatee (3) contribute to excessively positive reviews and this keeps the market fluid.

This paper calls for problematization of the idea of sharing in the so-called “sharing economy”. What is shared on peer-to-peer platforms is the comfort of engaging with people like ourselves.

This paper contributes to the literature on online accounting by extending consideration of evaluation beyond the review process. It also stresses that trust in the evaluative infrastructure is fostered by narcissistic relationships between users, who come to use the platform as a mirror. The peer-to-peer context refreshes the our knowledge on evaluation in a corporate context by highlighting phenomena of standardized spontaneity and euphemized evaluation language. This allows evaluation processes to incorporate a market logic without having to fuel competition.

Due to their very different contexts, the responses made by property hosts to online reviews can differ from those posted by hotel managers. Thus, the purpose of this study is to investigate the determinants of the responding behavior of hosts on peer-to-peer property rental platforms.

This study applied a comprehensive framework based on the theory of planned behavior. Empirical models are constructed based on 89,967 guest reviews with their associated responses to reveal the responding pattern of property hosts.

Unlike hotel managers, property hosts are more likely to reply to positive than to negative reviews; moreover, when they do choose to respond to negative reviews, they are likely to do so negatively, in a “tit-for-tat” way. This study also finds that one reason for the difference of responding patterns between property hosts and hotel managers is the hosts’ lack of experience of consumer relationship management and service recovery.

This study provides a good start point for future theoretical development regarding effective responding strategy on peer-to-peer property rental platforms, as well as some useful implications for practitioners.

This study is an early attempt to analyze the impact of the particularity of emerging platforms on the responding behavior of service providers based on a comprehensive conceptual framework and empirical model thus provides a good starting point for the further investigation of effective response strategies on these emerging platforms.

The purpose of this paper is to examine peer-to-peer sharing platform business models, their sources of competitive advantage, and the roles, motivations and behaviors of key actors in their ecosystems.

This paper uses a conceptual approach that is rooted in the service, tourism and hospitality, and strategy literature.

First, this paper defines key types of platform business models in the sharing economy anddescribes their characteristics. In particular, the authors propose the differentiation between sharing platforms of capacity-constrained vs capacity-unconstrained assets and advance five core properties of the former. Second, the authors contrast platform business models with their pipeline business model counterparts to understand the fundamental differences between them. One important conclusion is that platforms cater to vastly more heterogeneous assets and consumer needs and, therefore, require liquidity and analytics for high-quality matching. Third, the authors examine the competitive position of platforms and conclude that their widely taken “winner takes it all” assumption is not valid. Primary network effects are less important once a critical level of liquidity has been reached and may even turn negative if increased listings raise friction in the form of search costs. Once a critical level of liquidity has been reached, a platform’s competitive position depends on stakeholder trust and service provider and user loyalty. Fourth, the authors integrate and synthesize the literature on key platform stakeholders of platform businesses (i.e. users, service providers, and regulators) and their roles and motivations. Finally, directions for further research are advanced.

This paper helps platform owners, service providers and users understand better the implications of sharing platform business models and how to position themselves in such ecosystems.

This paper integrates the extant literature on sharing platforms, takes a novel approach in delineating their key properties and dimensions, and provides insights into the evolving and dynamic forms of sharing platforms including converging business models.

This study aims to focus on peer-to-peer accommodation services in the sharing economy. Adopting construal level theory as the theoretical foundation, this study investigates the main and interaction effects of social and spatial distances on guest loyalty toward peer-to-peer accommodation.

This study uses a secondary source of online observational data archived on Xiaozhu, a leading peer-to-peer accommodation sharing platform in China. It consists of 2,612 observations of 1,304 unique travelers who stayed at 559 listings managed by 281 hosts in four major metropolitan areas of China over four years from August 2012 to August 2016. Non-linear binary choice panel models of probability regressions were used to estimate the effects of psychological distances (social and spatial) between hosts and guests on the likelihood of repeat purchase. The software used for the econometric analyses is STATA 14.

The results indicate that social distance negatively affects guest loyalty toward the listing hosts, while spatial distance has a positive influence on guest loyalty. The results also show significant interactions between the two psychological distance dimensions in influencing loyalty. The findings provide important insight into the influences of psychological distances on travelers’ repeat purchase behavior toward peer-to-peer accommodation providers.

This study contributes to the literature by providing empirical evidence that supports the importance of psychological distances in forming a loyal relationship between hosts and guests in the peer-to-peer accommodation sector of the sharing economy.

This study aims to identify the individual and trip characteristics that are associated with intention to use peer-to-peer accommodation, including past experience (users vs non-users), accommodation preferences, traveler personality and tripographic variables.

To compare Airbnb users and non-users, quantitative research was conducted to test for group differences. A questionnaire was designed and administered face-to-face in major tourist areas. Quota sampling was used to ensure comparable samples of Airbnb users and non-users.

While Airbnb users and non-users expressed few differences in their demographics and perceived importance of accommodation attributes, the two groups vary in their perception of Airbnb and evaluation of Airbnb compared to hotels, suggesting some positive and negative changes after experiencing Airbnb. Respondents who were more allocentric were more likely to use Airbnb. Hotels were preferred for traveling with family as well as shorter trips, while Airbnb was preferred for traveling with friends as well as longer trips.

This study identified several challenges for Airbnb and other sharing platforms, including consumers’ security concerns, potential decrease in the likelihood of repeat usage and low likelihood of using Airbnb when traveling with family.

While previous studies focused more on existing customers of peer-to-peer accommodation, this study compared users and non-users and identified key differences in their perceptions. The use of traveler personality and tripographic variables to examine intention to use Airbnb provides a unique perspective to consider Airbnb as an “allocentric destination” and the type of trips that are more compatible with the Airbnb experience.

In recent years, some peer-to-peer (P2P) service sharing platforms have improved their service quality by setting an entry quality threshold for service providers. Considering consumers' heterogeneous preferences for service quality and commission rate, it is worth studying how to select the commission rate contract for a P2P platform under a predetermined entry quality threshold for service providers.

In this study, the platform's profit-maximizing model is constructed under two proposed contracts: unilateral commission rate contract and bilateral commission rate contract. The optimal entry quality threshold and the optimal commission rate are obtained. This study also explores the impacts of cross-side network externality and service price on a platform's optimal decisions and social welfare.

Results show that it is always advantageous for the platform to adopt the bilateral commission rate contract, which is closely related to the strength of cross-side network externality, service price and quality sensitivity coefficient. Under certain conditions, adopting the unilateral commission rate contract can reduce platform profit and service provider surplus, and improve consumer surplus and social welfare.

This study analyzes the unilateral commission rate contract and the bilateral commission rate contract of the platform, and discusses which contract is beneficial to the platform, consumers and service providers. In addition, this study provides a basis for the operation decision of a P2P service sharing platform and the pricing decision of service providers.

This study aims to address the following three questions: What are the main factors influencing co-creation behaviour among peers in a peer-to-peer (P2P) platform? What are the key consequences of such behaviour? and What are the main factors that positively influence a sense of commitment among peers in a P2P platform?

This study used a positivist paradigm to scrutinise the causal associations between the scale validation and causal configurations of influential factors by using fuzzy set qualitative comparative analysis.

Findings indicate that the significance of co-creation behaviour in enhancing the sense of commitment in a P2P platform. The implications for hospitality managers and researchers are discussed.

The findings of this research provide interesting insights for peer providers in a peer platform on how to enhance co-creation. They also offer guidelines on how to build a positive sense of commitment in the peer platform.

By investigating co-creation behaviour at the peer level, this research offers a unique theoretical contribution. Drawing on complexity theory, the research also proposes two tenets supporting the managerial contribution by identifying and clarifying how co-creation behaviour and related constructs can lead to a sense of commitment between peers in a P2P platform.

Trust has been widely recognized as the crucial factor of consumer purchase intention when shopping on peer-to-peer short-term rental platforms where hosts and renters are strangers. However, the specific attributes of hosts that help build trust with potential renters and drive their purchase of short-term rentals remain unknown. This study aims to explore the effects of host attributes on renter purchases made on Xiaozhu.com, one of the top short-term rental platforms in China, while controlling for short-term rental characteristics.

A crawler program was developed by Python to collect the host attributes and their short-term rental characteristics of 935 hosts in Beijing from November 18, 2015 to February 14, 2016. The authors use Poisson regression models to estimate the effects of host attributes on renter reservations. They also conduct a series of robustness checks for the estimated results.

The authors found that host attributes such as the time of reservation confirmation, the acceptance rate of renter reservations, the number of listings owned, whether a personal profile page is disclosed and gender of the host significantly affect renter reservations, whereas the response rate of the host does not influence renters when purchasing short-term rentals online.

This study identifies which host attributes are perceived as trustworthy and affect renters’ purchase decisions, a topic of both theoretical and practical importance but currently less researched. The findings add to emerging literature by providing insights on trust-building in the peer-to-peer economy. Useful suggestions are also provided on strengthening the trust mechanism on short-term rental platforms to facilitate peer-to-peer transactions. Notably, the study is the first attempt to examine the perception of Chinese users toward short-term rentals despite its global prevalence. The analytical insights revealed from large scale but granular online observations data of host attributes and actual renter reservations greatly supplement findings of extant literature using survey and experiment approaches.

This study aims to provide a critical reflection of the application of image recognition techniques in visual information mining in hospitality and tourism.

This study begins by reviewing the progress of image recognition and advantages of convolutional neural network-based image recognition models. Next, this study explains and exemplifies the mechanisms and functions of two relevant image recognition applications: object recognition and facial recognition. This study concludes by providing theoretical and practical implications and potential directions for future research.

After this study presents different potential applications and compares the use of image recognition with traditional manual methods, the main findings of this critical reflection revolve around the feasibility of the described techniques.

Knowledge on how to extract valuable visual information from large-scale user-generated photos to infer the online behavior of consumers and service providers and its influence on purchase decisions and firm performance is crucial to business practices in hospitality and tourism.

Visual information plays a crucial role in online travel agencies and peer-to-peer accommodation platforms from the side of sellers and buyers. However, extant studies relied heavily on traditional manual identification with small samples and subjective judgment. With the development of deep learning and computer vision techniques, current studies were able to extract various types of visual information from large-scale datasets with high accuracy and efficiency. To the best of the authors’ knowledge, this study is the first to offer an outlook of image recognition techniques for mining visual information in hospitality and tourism.

The aim of this article is to test the hypothesis that peer-to-peer technology platforms (Uber) are associated with disruption in the institutional environment, affecting beliefs, norms and users' ways of thinking and acting.

Probability sample comprising 843 users (446 passengers; 397 drivers) in the city of Belo Horizonte, Brazil, using a set of indicators was specifically designed for this study.

Uber triggers significant changes in the systems of rewards and sanctions, in social preferences, and in entrepreneurial structure and governance, and promotes the coexistence of an institutional logic, hitherto dominant, with new believes, rules, norms and regulatory systems.

This is a pioneer study that associates institutional approach's elements with technology platforms; the authors also elaborated and utilized an analysis model consisting of a set of completely original indicators capable of mapping and measuring different dimensions of the phenomenon under analysis.